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A single-cell atlas of circulating immune cells over the first 2 months of age in extremely premature infants

GSE271413 Homo sapiens Expression profiling by high throughput sequencing; Other 83 samples 2025/02/05 GPL24676
Summary
Extremely premature infants (EPI), or those born prior to 30 weeks’ gestation, are highly susceptible to infection and inflammation. The impact of pre- and postnatal exposures on the immune response is poorly understood. We analyzed over 200, 000 circulating immune cells from 100-200L of whole blood collected from 25 donors including longitudinal samples from 10 EPI that were compared to healthy adults and to cord blood from term and preterm infants. We performed single-cell RNA-, T- and B-cell receptor sequencing, and phosphoprotein mass cytometry from matched samples and proteomics on dried blood spots from the same donors. Our data defined the trajectory of circulating T-, B-, myeloid and natural killer cells. Peripheral T cell development rapidly progressed over the first month of EPI’s life with an increase in the proportion of naïve CD4+, regulatory, and cycling T cells, accompanied by increased STAT5 signaling. Simultaneously, the transcription of IL2, which is essential for T cell activation and proliferation, increased in the lymphocytes. T cell repertoire diversity, clonality and gene usage increased rapidly over the first 60 days of life in EPI. CD38hi B cells were directly proportional to gestational age at birth. Interestingly, in utero exposure to inflammation (chorioamnionitis) altered the abundance, transcriptome and signaling of circulating T cells and monocytes specifically at 1 month of life in EPIs. Finally, we demonstrate the feasibility of a robust multi-omic longitudinal analysis in EPI from minute amounts of blood, developing a resource for studying the impact of early life exposures on immune development.
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NCBI GEO page ↗ Paper (PMID 40043141) ↗ {# Names what the click gives you. "Open in finder" meant nothing to a visitor who arrived from a search engine and has never seen the tool. #} Find more human RNA-seq datasets →
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